Language agnostic code classification

ABSTRACT

A system may include a computer processor and a repository configured to store a first code fragment including language features represented in a first programming language, and a second code fragment including language features represented in a second programming language. The system may further include a universal code fragment classifier, executing on the computer processor and configured to generate a first universal abstract syntax tree for the first code fragment and a second universal abstract syntax tree for the second code fragment, generate, using a graph embedding model, first vectors for the first universal abstract syntax tree and second vectors for the second universal abstract syntax tree, and classify, by executing an abstract syntax tree classifier on the first vectors and the second vectors, the first code fragment as a first code category and the second code fragment as a second code category.

BACKGROUND

Conventional machine learning models are limited to classifying coderepresented in a single programming language. However, it may be thecase that available training data is represented in a programminglanguage different from the programming language of the code to whichthe machine learning model is applied. Thus, a language independent codeclassification capability that generalizes learning beyond a singleprogramming language is desirable.

SUMMARY

This summary is provided to introduce a selection of concepts that arefurther described below in the detailed description. This summary is notintended to identify key or essential features of the claimed subjectmatter, nor is it intended to be used as an aid in limiting the scope ofthe claimed subject matter.

In general, in one aspect, one or more embodiments relate to a systemincluding a computer processor, a repository configured to store a firstcode fragment including language features represented in a firstprogramming language, a second code fragment including language featuresrepresented in a second programming language, a first universal abstractsyntax tree for the first code fragment, and a second universal abstractsyntax tree for the second code fragment. The first universal abstractsyntax tree and the second universal abstract syntax tree each includeat least one language independent feature. The system further includes auniversal code fragment classifier, executing on the computer processorand configured to generate the first universal abstract syntax tree forthe first code fragment and the second universal abstract syntax treefor the second code fragment, generate, using a graph embedding model,first vectors for the first universal abstract syntax tree and secondvectors for the second universal abstract syntax tree, and classify, byexecuting an abstract syntax tree classifier on the first vectors andthe second vectors, the first code fragment as a first code category andthe second code fragment as a second code category.

In general, in one aspect, one or more embodiments relate to a methodfor obtaining a first code fragment including language featuresrepresented in a first programming language and a second code fragmentincluding language features represented in a second programminglanguage, and generating a first universal abstract syntax tree for thefirst code fragment and a second universal abstract syntax tree for thesecond code fragment. The first universal abstract syntax tree and thesecond universal abstract syntax tree each include at least one languageindependent feature. The method further includes generating, using agraph embedding model, first vectors for the first universal abstractsyntax tree and second vectors for the second universal abstract syntaxtree, and classifying, by executing an abstract syntax tree classifieron the first vectors and the second vectors, the first code fragment asa first code category and the second code fragment as a second codecategory.

In general, in one aspect, one or more embodiments relate to a methodfor obtaining a code fragment including language features represented ina programming language, and sending the code fragment to a universalcode fragment classifier configured to perform generating a firstuniversal abstract syntax tree for the first code fragment. Theuniversal abstract syntax tree includes at least one languageindependent feature. The universal code fragment classifier is furtherconfigured to perform generating, using a graph embedding model, vectorsfor the universal abstract syntax tree, classifying, by executing anabstract syntax tree classifier on the vectors, the code fragment as acode category, and transmitting the code category for the code fragment.The method further includes receiving, from the universal code fragmentclassifier, the code category for the code fragment.

Other aspects of the invention will be apparent from the followingdescription and the appended claims.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1A and FIG. 1B show a system in accordance with one or moreembodiments of the invention.

FIG. 2, FIG. 3A, and FIG. 3B show flowcharts in accordance with one ormore embodiments of the invention.

FIG. 4A, FIG. 4B, and FIG. 4C show examples in accordance with one ormore embodiments of the invention.

FIG. 5A and FIG. 5B show computing systems in accordance with one ormore embodiments of the invention.

DETAILED DESCRIPTION

Specific embodiments of the invention will now be described in detailwith reference to the accompanying figures. Like elements in the variousfigures are denoted by like reference numerals for consistency.

In the following detailed description of embodiments of the invention,numerous specific details are set forth in order to provide a morethorough understanding of the invention. However, it will be apparent toone of ordinary skill in the art that the invention may be practicedwithout these specific details. In other instances, well-known featureshave not been described in detail to avoid unnecessarily complicatingthe description.

Throughout the application, ordinal numbers (e.g., first, second, third,etc.) may be used as an adjective for an element (i.e., any noun in theapplication). The use of ordinal numbers is not to imply or create anyparticular ordering of the elements nor to limit any element to beingonly a single element unless expressly disclosed, such as by the use ofthe terms “before”, “after”, “single”, and other such terminology.Rather, the use of ordinal numbers is to distinguish between theelements. By way of an example, a first element is distinct from asecond element, and the first element may encompass more than oneelement and succeed (or precede) the second element in an ordering ofelements.

In general, embodiments of the invention are directed to classifyingcode fragments. A universal abstract syntax tree is generated for eachcode fragment. The universal abstract syntax tree is a flexiblestructure whose schema accommodates code fragments represented indifferent programming languages. In one or more embodiments, vectors aregenerated for subtrees of the universal abstract syntax tree using agraph embedding model. The subtrees may correspond to high-levelprogramming language features such as control flow features and/ordeclaration features. A control flow feature corresponds to a break inthe sequential execution of statements within a code fragment. Adeclaration feature declares a high-level language feature, such as aclass, a function, etc. The graph embedding model may aggregateinformation about language features from nodes in a local neighborhoodof a subtree. The vectors are classified as a code category. Forexample, the code category may be a security relevant category (e.g.,taint source or taint sink) or a category relevant to programcomprehension (e.g., a cryptographic algorithm or a sorting algorithm).The vectors may be classified by a machine learning model trained usingtraining code fragments that include code fragments represented inmultiple programming languages. The classifier may include thecapability to generalize knowledge learned from training code fragmentsto code fragments represented in an “unseen” programming language forwhich there were no training code fragments.

FIG. 1A shows a system (100) in accordance with one or more embodimentsof the invention. As shown in FIG. 1A, the system (100) includes aback-end computer system (101) and a user computing system (102). In oneor more embodiments, the back-end computer system (101) and the usercomputing system (102) take the form of the computing system (500)described with respect to FIG. 5A and the accompanying description belowor take the form of the client device (526) described with respect toFIG. 5B. The back-end computer system (101) includes a repository (104),a universal code fragment classifier (106), and computer processor(s)(108).

In one or more embodiments, the repository (104) is any type of storageunit and/or device (e.g., a file system, database, collection of tables,or any other storage mechanism) for storing data. Further, therepository (104) may include multiple different storage units and/ordevices. The multiple different storage units and/or devices may or maynot be of the same type or located at the same physical site. Therepository (104) may be accessed online via a cloud service (e.g.,Amazon Web Services (AWS), Egnyte, Azure, etc.).

In one or more embodiments, the repository (104) includes functionalityto store code fragments (110A, 110N) and universal abstract syntax trees(116A, 116N). Code fragments (110A, 110N) are collections of computerinstructions (e.g., statements) written in source code of ahuman-readable programming language. Code fragments (110A, 110N) includelanguage features (114L, 114X). Language features (114L) are syntacticconstructs included in the code fragment (110A). Examples of languagefeatures (114L) include operators, expressions, statements, classes,declarations, methods, functions, interfaces, packages, commands,identifiers, etc.

Each code fragment (110A) corresponds to a universal abstract syntaxtree (116A). The universal abstract syntax tree (116A) is a treerepresentation of the syntactical structure of the corresponding codefragment (110A). The universal abstract syntax tree (116A) includes aschema capable of representing code fragments (110A, 110N) of multipleprogramming languages. The universal abstract syntax tree (116A) is“abstract” in the sense that the universal abstract syntax tree (116A)includes structural and/or content-related details of the code fragment(110A) while omitting one or more syntactic details of the code fragment(110A). For example, because grouping parentheses are implicit in thetree structure of the universal abstract syntax tree (116A), theuniversal abstract syntax tree (116A) may omit nodes corresponding tothe grouping parentheses. The nodes of the universal abstract syntaxtree (116A) may be generated from the code fragment (110A) by a compileror a parser.

Turning to FIG. 1B, the universal abstract syntax tree (150) includes aroot node (152) and subtrees (e.g., subtree B (154B) and subtree F(154F)). Subtree B (154B) and subtree F (154F) are distinct from eachother, but they are both specified as having a relationship to the rootnode (152). Additional subtrees may be present which branch out fromsubtree B (154B) and/or subtree F (154F), and/or the root node (152).The root node (152) may include a language feature that corresponds to astarting point of a code fragment (110A). For example, the root node(152) may include a language feature called “module”, “file”, “program”,or “compilation unit,” etc. Each subtree includes a subset of the nodesof the universal abstract syntax tree (150). A subtree may be definedrecursively. That is, the nodes of a subtree may, in turn, include aroot node of the subtree and subtrees of the subtree, etc.Alternatively, the subtree may include a single node, such as a nodethat includes a simple language feature (e.g., an identifier or aconstant).

A subtree (154B) may include a language specific feature (156) that isspecific to a programming language. That is, the language specificfeature (156) may be specific to the programming language in which thecode fragment (110A) corresponding to the universal abstract syntax tree(150) is represented. For example, the language specific feature may bean “assignment” feature that is specific to Python or Java.Alternatively, a subtree (154F) may include a language independentfeature (158) that is common across multiple programming languages, suchas an “identifier” feature that is used in Python, Java, and otherprogramming languages.

Returning to FIG. 1A, the universal code fragment classifier (106)includes a graph embedding model (120) and an abstract syntax treeclassifier (122). The graph embedding model (120) includes functionalityto embed a subtree (e.g., subtree B (154B) or subtree F (154F)) as avector. In one or more embodiments, the graph embedding model (120) is amachine learning model that includes functionality to learn a functionthat generates a vector for a first node by sampling and aggregatinginformation about language features from nodes in the local neighborhoodof the first node. For example, the first node may be a root node of asubtree, and the nodes in the local neighborhood may include descendantnodes (e.g., child nodes, grandchild nodes, etc.) of the root node ofthe subtree and/or ancestor nodes (e.g., parent nodes, grandparentnodes, etc.) of the root node of the subtree. The graph embedding model(120) may implemented using a graph neural network (e.g., the GraphSAGEneural network) that includes functionality to generate embeddings(e.g., vectors) for graph structures. The graph embedding model (120)includes functionality to generate similar embeddings for subtrees thatshare similar contexts (e.g., subtrees that include similar languagefeatures).

The universal code fragment classifier (106) includes functionality toapply the graph embedding model (120) to generate a vector for a subtree(e.g., subtree B (154B) or subtree F (154F)). In one or moreembodiments, the subtree is a high-level subtree that includes ahigh-level language feature. For example, the root node of thehigh-level subtree may correspond to the high-level language feature.The high-level language feature may be a control flow feature thatcorresponds to a break in the sequential execution of statements in theportion of the code fragment (110A) corresponding to the subtree. Thatis, executing the control flow feature may cause execution to jump to alocation in the code fragment (110A) other than the next sequentialstatement in the code fragment (110A). For example, the control flowfeature may be an iteration (e.g., loop) statement or a conditionalbranch statement. Alternatively, the high-level language feature may bea declaration feature. The declaration feature may declare a high-levellanguage feature, such as a class, a method, a function, a procedure,etc. The high-level language feature may be a language specific feature.For example, FIG. 4B shows a universal abstract syntax tree (402B) thatincludes the following subtrees corresponding to high-level languagespecific features: a Java method declaration subtree (432) and a Javavariable declaration subtree (436). Alternatively, the high-levellanguage feature may be a language independent feature. For example,FIG. 4B shows a universal abstract syntax tree (402B) that includes asubtree corresponding to a high-level language independent feature: auniversal block subtree (434).

The abstract syntax tree classifier (122) may be a neural network model.The abstract syntax tree classifier (122) includes functionality toclassify, as a code category (132), a collection (e.g., a sequence) ofinput vectors. The collection of input vectors may be generated from auniversal abstract syntax tree (116A). The code category (132) maycorrespond to a function performed by the code fragment (110A)corresponding to the universal abstract syntax tree (116A).

Each input vector may correspond to a high-level subtree of theuniversal abstract syntax tree (116A). In one or more embodiments,generating vectors for high-level subtrees that include high-levellanguage features focuses the abstract syntax tree classifier (122) onthe high-level function(s) performed by the code fragment (110A)corresponding to the universal abstract syntax tree (116A). In one ormore embodiments, the abstract syntax tree classifier (122) assignsconfidence levels where each confidence level indicates a probabilitythat the corresponding code category describes a function performed bythe code fragment corresponding to the universal abstract syntax tree.

For example, the code category (132) may be a security category relevantto performing a security analysis (130). Continuing this example, thesecurity category may be “taint source”, “taint sink”, or “sanitizer.”The security analysis (130) may analyze a codebase to detect securityvulnerabilities. For example, a security vulnerability may correspond toa flow of “tainted” data from a taint source to a taint sink withoutprocessing the data by a sanitizer. A taint source may receive potentialattacker-controllable input. A taint sink may perform asecurity-sensitive operation (e.g., by executing the JavaScript evaloperator). A sanitizer is a statement or function that transforms apotentially tainted value into a safe (e.g., trusted) value that is notassociated with a security vulnerability. For example, a sanitizer maymodify a value by encoding or replacing potentially dangerous characterswith harmless equivalents. Continuing this example, the potentiallydangerous characters may be filtered or escaped.

As another example, the code category (132) may be a category relevantto program comprehension (e.g., by a debugger), such as a cryptographicalgorithm or a sorting algorithm. Alternatively, the code category (132)may be a code defect category, such as variable misuse. Stillalternatively, the code category (132) may be a name of the codefragment (110A), such as a variable or method name.

The abstract syntax tree classifier (122) may be trained using trainingcode fragments that include code fragments represented in multipleprogramming languages. The abstract syntax tree classifier (122) mayinclude functionality to perform transfer learning. That is, theabstract syntax tree classifier (122) may include the capability togeneralize knowledge learned from training code fragments to codefragments represented in an “unseen” programming language for whichthere were no training code fragments. In other words, the training codefragments may exclude code fragments represented in the unseenprogramming language. Thus, training the abstract syntax tree classifier(122) is scalable because the training code fragments are not requiredto include code fragments in each programming language that the abstractsyntax tree classifier (122) is intended to support.

Continuing with FIG. 1A, the user computing system (102) may be a mobiledevice (e.g., phone, tablet, digital assistant, laptop, etc.) or anyother computing device (e.g., desktop, terminal, workstation, etc.) witha computer processor (not shown) and memory (not shown) capable ofrunning computer software. The user computing system (102) includesfunctionality to send a code fragment (110K) to the universal codefragment classifier (106). The user computing system (102) includesfunctionality to receive a code category (132) for the code fragment(110K) from the universal code fragment classifier (106). The usercomputing system (102) includes functionality to perform a securityanalysis (130) using the code fragment (110K).

In one or more embodiments, the computer processor(s) (108) takes theform of the computer processor(s) (502) described with respect to FIG.5A and the accompanying description below. In one or more embodiments,the computer processor (108) includes functionality to execute theuniversal code fragment classifier (106).

While FIG. 1A shows a configuration of components, other configurationsmay be used without departing from the scope of the invention. Forexample, various components may be combined to create a singlecomponent. As another example, the functionality performed by a singlecomponent may be performed by two or more components.

FIG. 2 shows a flowchart in accordance with one or more embodiments ofthe invention. The flowchart depicts a process for classifying a codefragment. One or more of the steps in FIG. 2 may be performed by thecomponents (e.g., the universal code fragment classifier (106) of theback-end computer system (101) and/or the user computing system (102),discussed above in reference to FIG. 1A). In one or more embodiments ofthe invention, one or more of the steps shown in FIG. 2 may be omitted,repeated, and/or performed in parallel, or in a different order than theorder shown in FIG. 2. Accordingly, the scope of the invention shouldnot be considered limited to the specific arrangement of steps shown inFIG. 2.

Initially, in Step 202, a first code fragment including first languagefeatures represented in a first programming language and a second codefragment including second language features represented in a secondprogramming language are obtained. The first code fragment and thesecond code fragment may be obtained from a repository. The firstlanguage features and the second language features may be operators,expressions, statements, and/or other syntactic constructs included inthe first code fragment and the second code fragment.

In Step 204, a first universal abstract syntax tree for the first codefragment and a second universal abstract syntax tree for the second codefragment are generated. The first universal abstract syntax tree and thesecond universal abstract syntax tree may include language specificand/or language independent features. The first universal abstractsyntax tree and the second universal abstract syntax tree may includeone or more high-level subtrees that include high-level features.Examples of high-level features include control flow features anddeclaration features.

Prior to generating a universal abstract syntax tree, the universal codefragment classifier may generate a language specific abstract syntaxtree for a code fragment. Then, the universal code fragment classifiermay convert the language specific abstract syntax tree to a universalabstract syntax tree. For example, the universal code fragmentclassifier may generate a universal abstract syntax tree from thelanguage specific abstract syntax tree using a universal source codeparser.

In Step 206, first vectors for the first universal abstract syntax treeand second vectors for the second universal abstract syntax tree aregenerated using a graph embedding model. In one or more embodiments, thefirst vectors and the second vectors are vectors generated forhigh-level subtrees that include high-level features (see Step 204above). For example, the universal code fragment classifier may use thegraph embedding model to embed each of the high-level subtrees as asingle unit.

In Step 208, the first code fragment is classified as a first codecategory and the second code fragment is classified as a second codecategory by executing an abstract syntax tree classifier on the firstvectors and the second vectors. In one or more embodiments, generatingthe first vectors and the second vectors for high-level subtrees thatinclude high-level language features (as described in Step 206 above)focuses the abstract syntax tree classifier on the high-levelfunction(s) performed by the first code fragment and the second codefragment. In one or more embodiments, the abstract syntax treeclassifier classifies a code fragment as a code category based onconfidence levels assigned to different code categories where eachconfidence level indicates a probability that the corresponding codecategory describes a function performed by the respective code fragment.For example, a code fragment may be classified as the code categorycorresponding to the highest confidence level.

In one or more embodiments, the abstract syntax tree classifier fails toclassify the first code fragment and/or the second code fragment as anycode category when none of the confidence levels assigned to a codecategory exceeds a threshold.

In one or more embodiments, a security analysis may be performed (e.g.,by the user computing system) using the first code fragment and/or thesecond code fragment. For example, the first code category and/or thesecond code category may be a category relevant to performing thesecurity analysis. For example, the security category may “taintsource”, “taint sink”, or “sanitizer.”

FIG. 3A shows a flowchart in accordance with one or more embodiments ofthe invention. The flowchart depicts a process for classifying a codefragment. One or more of the steps in FIG. 3A may be performed by thecomponents (e.g., the universal code fragment classifier (106) of theback-end computer system (101) and/or the user computing system (102),discussed above in reference to FIG. 1A). In one or more embodiments ofthe invention, one or more of the steps shown in FIG. 3A may be omitted,repeated, and/or performed in parallel, or in a different order than theorder shown in FIG. 3A. Accordingly, the scope of the invention shouldnot be considered limited to the specific arrangement of steps shown inFIG. 3A.

Initially, in Step 302, a code fragment including language featuresrepresented in a programming language is obtained (see description ofStep 202 above).

In Step 304, the code fragment is sent to a universal code fragmentclassifier configured to classify the code fragment as a code categoryby performing the steps of FIG. 3B. The code fragment may be sent to theuniversal code fragment classifier via a network (e.g., network (520) ofFIG. 5B).

In Step 306, the code category for the code fragment is received fromthe universal code fragment classifier. The code category for the codefragment may be received from the universal code fragment classifier viathe network.

FIG. 3B shows a flowchart in accordance with one or more embodiments ofthe invention. The flowchart depicts a process for classifying a codefragment. One or more of the steps in FIG. 3B may be performed by thecomponents (e.g., the universal code fragment classifier (106) of theback-end computer system (101) and/or the user computing system (102),discussed above in reference to FIG. 1A). In one or more embodiments ofthe invention, one or more of the steps shown in FIG. 3B may be omitted,repeated, and/or performed in parallel, or in a different order than theorder shown in FIG. 3B. Accordingly, the scope of the invention shouldnot be considered limited to the specific arrangement of steps shown inFIG. 3B.

Initially, in Step 352, a universal abstract syntax tree for the codefragment is generated (see description of Step 204 above).

In Step 354, vectors for the universal abstract syntax tree aregenerated using a graph embedding model (see description of Step 206above).

In Step 356, the code fragment is classified as the code category byexecuting an abstract syntax tree classifier on the vectors (seedescription of Step 208 above).

In Step 358, the code category for the code fragment is transmitted. Thecode category for the code fragment may be transmitted by the universalcode fragment classifier via the network.

FIG. 4A, FIG. 4B, and FIG. 4C show an implementation example(s) inaccordance with one or more embodiments. The implementation example(s)are for explanatory purposes only and not intended to limit the scope ofthe invention. One skilled in the art will appreciate thatimplementation of embodiments of the invention may take various formsand still be within the scope of the invention.

FIG. 4A shows Python code fragment A (400A) ((110A, 110N, 110K) in FIG.1A) that includes Python language features, including Python assignmentfeature “z=x*y” (401) ((114L, 114X) in FIG. 1A). Python assignmentfeature “z=x*y” (401) is a Python-specific language feature thatincludes a Python-specific expression sub-feature “x*y” and a universal(e.g., Python-independent) identifier sub-feature “z”. The universalcode fragment classifier generates universal abstract syntax tree A(402A) ((116A, 116N) in FIG. 1A and (150) in FIG. 1B) from Python codefragment A (400A). Universal abstract syntax tree A (402A) includesPython assignment subtree (406), which corresponds to Python assignmentfeature “z=x*y” (401). Python assignment subtree (406) includes Pythonexpression subtree (408), which corresponds to the expressionsub-feature “x*y” of Python assignment feature “z=x*y” (401). Universalabstract syntax tree A (402A) also includes universal identifier node A(410A), which corresponds to the sub-feature “z” in Python assignmentfeature “z=x*y” (401).

FIG. 4B shows a Java code fragment (420) that includes a Java methoddeclaration feature (422). Java method declaration feature (422) is aJava-specific language feature that includes a universal (e.g.,Java-independent) block sub-feature (424) corresponding to the function(e.g., method) body. The universal block sub-feature (424) includesJava-specific variable declaration sub-features, including aJava-specific variable declaration sub-feature for the variable “z”.

The universal code fragment classifier generates universal abstractsyntax tree B (402B) from the Java code fragment (420). Universalabstract syntax tree B (402B) includes Java method declaration subtree(432), which corresponds to the Java method declaration feature (422).Java method declaration subtree (432) includes universal block subtree(434), which corresponds to the universal block sub-feature (424).Universal abstract syntax tree B (402B) also includes Java variabledeclaration subtree (436), which corresponds to the Java-specificvariable declaration sub-feature for the variable “z”. Universalabstract syntax tree B (402B) also includes universal identifier node B(410B), which corresponds to a universal identifier sub-feature withinuniversal block sub-feature (424). Universal identifier node B (410B)corresponds to a variable declared within the universal blocksub-feature (424).

FIG. 4C shows a Python code fragment B (400B) that includes high-levelcontrol flow features, including a “while” feature, a “for” feature, andan “if” feature. The universal code fragment classifier generatesuniversal abstract syntax tree C (402C) from Python code fragment B(400B). Universal abstract syntax tree C (402C) includes the followingsubtrees corresponding to the high-level control flow features:

1) While subtree (450), corresponding to the “while” feature;

2) For subtree (452), corresponding to the “for” feature;

3) If subtree J (454), corresponding to a branch condition sub-featureof the “for” feature; and

4) If subtree K (456), corresponding to the “if” feature.

The universal code fragment classifier generates, using the graphembedding model, vectors for the above subtrees. The universal codefragment classifier then classifies, by executing the abstract syntaxtree classifier on the vectors, Python code fragment B (400B) as a“bubblesort” code category. In this scenario, the abstract syntax treeclassifier is a neural network trained with training code fragmentsrepresented in multiple languages. However, the abstract syntax treeclassifier was not trained with any Python code fragments. Even thoughthe abstract syntax tree classifier was not trained with Python codefragments, the abstract syntax tree classifier is able to classifyPython code fragment B (400B).

Embodiments disclosed herein may be implemented on a computing systemspecifically designed to achieve an improved technological result. Whenimplemented in a computing system, the features and elements of thisdisclosure provide a significant technological advancement overcomputing systems that do not implement the features and elements of thedisclosure. Any combination of mobile, desktop, server, router, switch,embedded device, or other types of hardware may be improved by includingthe features and elements described in the disclosure. For example, asshown in FIG. 5A, the computing system (500) may include one or morecomputer processors (502), non-persistent storage (504) (e.g., volatilememory, such as random access memory (RAM), cache memory), persistentstorage (506) (e.g., a hard disk, an optical drive such as a compactdisk (CD) drive or digital versatile disk (DVD) drive, a flash memory,etc.), a communication interface (512) (e.g., Bluetooth interface,infrared interface, network interface, optical interface, etc.), andnumerous other elements and functionalities that implement the featuresand elements of the disclosure.

The computer processor(s) (502) may be an integrated circuit forprocessing instructions. For example, the computer processor(s) may beone or more cores or micro-cores of a processor. The computing system(500) may also include one or more input devices (510), such as atouchscreen, keyboard, mouse, microphone, touchpad, electronic pen, orany other type of input device.

The communication interface (512) may include an integrated circuit forconnecting the computing system (500) to a network (not shown) (e.g., alocal area network (LAN), a wide area network (WAN) such as theInternet, mobile network, or any other type of network) and/or toanother device, such as another computing device.

Further, the computing system (500) may include one or more outputdevices (508), such as a screen (e.g., a liquid crystal display (LCD), aplasma display, touchscreen, cathode ray tube (CRT) monitor, projector,or other display device), a printer, external storage, or any otheroutput device. One or more of the output devices may be the same ordifferent from the input device(s). The input and output device(s) maybe locally or remotely connected to the computer processor(s) (502),non-persistent storage (504), and persistent storage (506). Manydifferent types of computing systems exist, and the aforementioned inputand output device(s) may take other forms.

Software instructions in the form of computer readable program code toperform embodiments disclosed herein may be stored, in whole or in part,temporarily or permanently, on a non-transitory computer readable mediumsuch as a CD, DVD, storage device, a diskette, a tape, flash memory,physical memory, or any other computer readable storage medium.Specifically, the software instructions may correspond to computerreadable program code that, when executed by a processor(s), isconfigured to perform one or more embodiments disclosed herein.

The computing system (500) in FIG. 5A may be connected to or be a partof a network. For example, as shown in FIG. 5B, the network (520) mayinclude multiple nodes (e.g., node X (522), node Y (524)). Each node maycorrespond to a computing system, such as the computing system shown inFIG. 5A, or a group of nodes combined may correspond to the computingsystem shown in FIG. 5A. By way of an example, embodiments disclosedherein may be implemented on a node of a distributed system that isconnected to other nodes. By way of another example, embodimentsdisclosed herein may be implemented on a distributed computing systemhaving multiple nodes, where each portion disclosed herein may belocated on a different node within the distributed computing system.Further, one or more elements of the aforementioned computing system(500) may be located at a remote location and connected to the otherelements over a network.

Although not shown in FIG. 5B, the node may correspond to a blade in aserver chassis that is connected to other nodes via a backplane. By wayof another example, the node may correspond to a server in a datacenter. By way of another example, the node may correspond to a computerprocessor or micro-core of a computer processor with shared memoryand/or resources.

The nodes (e.g., node X (522), node Y (524)) in the network (520) may beconfigured to provide services for a client device (526). For example,the nodes may be part of a cloud computing system. The nodes may includefunctionality to receive requests from the client device (526) andtransmit responses to the client device (526). The client device (526)may be a computing system, such as the computing system shown in FIG.5A. Further, the client device (526) may include and/or perform all or aportion of one or more embodiments disclosed herein.

The computing system or group of computing systems described in FIGS. 5Aand 5B may include functionality to perform a variety of operationsdisclosed herein. For example, the computing system(s) may performcommunication between processes on the same or different system. Avariety of mechanisms, employing some form of active or passivecommunication, may facilitate the exchange of data between processes onthe same device. Examples representative of these inter-processcommunications include, but are not limited to, the implementation of afile, a signal, a socket, a message queue, a pipeline, a semaphore,shared memory, message passing, and a memory-mapped file. Furtherdetails pertaining to a couple of these non-limiting examples areprovided below.

Based on the client-server networking model, sockets may serve asinterfaces or communication channel end-points enabling bidirectionaldata transfer between processes on the same device. Foremost, followingthe client-server networking model, a server process (e.g., a processthat provides data) may create a first socket object. Next, the serverprocess binds the first socket object, thereby associating the firstsocket object with a unique name and/or address. After creating andbinding the first socket object, the server process then waits andlistens for incoming connection requests from one or more clientprocesses (e.g., processes that seek data). At this point, when a clientprocess wishes to obtain data from a server process, the client processstarts by creating a second socket object. The client process thenproceeds to generate a connection request that includes at least thesecond socket object and the unique name and/or address associated withthe first socket object. The client process then transmits theconnection request to the server process. Depending on availability, theserver process may accept the connection request, establishing acommunication channel with the client process, or the server process,busy in handling other operations, may queue the connection request in abuffer until server process is ready. An established connection informsthe client process that communications may commence. In response, theclient process may generate a data request specifying the data that theclient process wishes to obtain. The data request is subsequentlytransmitted to the server process. Upon receiving the data request, theserver process analyzes the request and gathers the requested data.Finally, the server process then generates a reply including at leastthe requested data and transmits the reply to the client process. Thedata may be transferred, more commonly, as datagrams or a stream ofcharacters (e.g., bytes).

Shared memory refers to the allocation of virtual memory space in orderto substantiate a mechanism for which data may be communicated and/oraccessed by multiple processes. In implementing shared memory, aninitializing process first creates a shareable segment in persistent ornon-persistent storage. Post creation, the initializing process thenmounts the shareable segment, subsequently mapping the shareable segmentinto the address space associated with the initializing process.Following the mounting, the initializing process proceeds to identifyand grant access permission to one or more authorized processes that mayalso write and read data to and from the shareable segment. Changes madeto the data in the shareable segment by one process may immediatelyaffect other processes, which are also linked to the shareable segment.Further, when one of the authorized processes accesses the shareablesegment, the shareable segment maps to the address space of thatauthorized process. Often, only one authorized process may mount theshareable segment, other than the initializing process, at any giventime.

Other techniques may be used to share data, such as the various datadescribed in the present application, between processes withoutdeparting from the scope of the invention. The processes may be part ofthe same or different application and may execute on the same ordifferent computing system.

The computing system in FIG. 5A may implement and/or be connected to adata repository. For example, one type of data repository is a database.A database is a collection of information configured for ease of dataretrieval, modification, re-organization, and deletion. DatabaseManagement System (DBMS) is a software application that provides aninterface for users to define, create, query, update, or administerdatabases.

The user, or software application, may submit a statement or query intothe DBMS. Then the DBMS interprets the statement. The statement may be aselect statement to request information, update statement, createstatement, delete statement, etc. Moreover, the statement may includeparameters that specify data, or data container (database, table,record, column, view, etc.), identifier(s), conditions (comparisonoperators), functions (e.g. join, full join, count, average, etc.), sort(e.g. ascending, descending), or others. The DBMS may execute thestatement. For example, the DBMS may access a memory buffer, a referenceor index a file for read, write, deletion, or any combination thereof,for responding to the statement. The DBMS may load the data frompersistent or non-persistent storage and perform computations to respondto the query. The DBMS may return the result(s) to the user or softwareapplication.

The above description of functions presents only a few examples offunctions performed by the computing system of FIG. 5A and the nodesand/or client device in FIG. 5B. Other functions may be performed usingone or more embodiments disclosed herein.

While the invention has been described with respect to a limited numberof embodiments, those skilled in the art, having benefit of thisdisclosure, will appreciate that other embodiments can be devised whichdo not depart from the scope of the invention as disclosed herein.Accordingly, the scope of the invention should be limited only by theattached claims.

What is claimed is:
 1. A system comprising: a computer processor; arepository configured to store: a first code fragment comprising a firstplurality of language features represented in a first programminglanguage, a second code fragment comprising a second pluralityrepresented in language features of a second programming language, afirst universal abstract syntax tree for the first code fragment, and asecond universal abstract syntax tree for the second code fragment,wherein the first universal abstract syntax tree and the seconduniversal abstract syntax tree each comprise at least one languageindependent feature; and a universal code fragment classifier executingon the computer processor and configured to: generate the firstuniversal abstract syntax tree for the first code fragment and thesecond universal abstract syntax tree for the second code fragment,generate, using a graph embedding model, a first plurality of vectorsfor the first universal abstract syntax tree and a second plurality ofvectors for the second universal abstract syntax tree, and classify, byexecuting an abstract syntax tree classifier on the first plurality ofvectors and the second plurality of vectors, the first code fragment asa first code category and the second code fragment as a second codecategory.
 2. The system of claim 1, wherein the universal code fragmentclassifier is further configured to: in response to classifying thefirst code fragment as the first code category, perform a securityanalysis using the first code fragment, wherein the first code categoryis a security relevant category.
 3. The system of claim 1, wherein theuniversal code fragment classifier is further configured to: train theabstract syntax tree classifier using training code fragments comprisingfirst code fragments represented in the first programming language andsecond code fragments represented in the second programming language. 4.The system of claim 3, wherein the universal code fragment classifier isfurther configured to: obtain a third code fragment comprising thirdlanguage features represented in a third programming language, whereinthe training code fragments exclude code fragments represented in thethird programming language, generate a third universal abstract syntaxtree for the third code fragment, wherein the third universal abstractsyntax tree comprises at least one language independent feature,generate, using the graph embedding model, a third plurality of vectorsfor the third universal abstract syntax tree, and classify, by executingthe abstract syntax tree classifier on the third plurality of vectors,the code fragment as a third code category.
 5. The system of claim 1,wherein the first universal abstract syntax tree comprises a subtree fora first language feature of the first plurality of language features,wherein the first language feature is one selected from a groupconsisting of a control flow statement and a declaration statement, andwherein the first plurality of vectors comprises a vector for thesubtree.
 6. The system of claim 5, wherein the first universal abstractsyntax tree comprises a plurality of nodes, wherein the subtreecomprises a first node of the plurality of nodes, and wherein theuniversal code fragment classifier is further configured to generate thefirst plurality of vectors by: identifying, for the first node, a subsetof the plurality of nodes in a local neighborhood of the first node, andaggregating information from a subset of the first plurality of languagefeatures corresponding to the subset of the plurality of nodes in thelocal neighborhood of the first node.
 7. The system of claim 1, whereinthe universal code fragment classifier is further configured to:generate a language-specific abstract syntax tree for the first codefragment and the first programming language, and convert thelanguage-specific abstract syntax tree to the first universal abstractsyntax tree.
 8. The system of claim 1, wherein the first universalabstract syntax tree further comprises a first language-specific featurethat is specific to the first programming language, and wherein thesecond universal abstract syntax tree further comprises a secondlanguage-specific feature that is specific to the second programminglanguage.
 9. A method comprising: obtaining a first code fragmentcomprising a first plurality of language features represented in a firstprogramming language and a second code fragment comprising a secondplurality of language features represented in a second programminglanguage; generating a first universal abstract syntax tree for thefirst code fragment and a second universal abstract syntax tree for thesecond code fragment, wherein the first universal abstract syntax treeand the second universal abstract syntax tree each comprise at least onelanguage independent feature; generating, using a graph embedding model,a first plurality of vectors for the first universal abstract syntaxtree and a second plurality of vectors for the second universal abstractsyntax tree; and classifying, by executing an abstract syntax treeclassifier on the first plurality of vectors and the second plurality ofvectors, the first code fragment as a first code category and the secondcode fragment as a second code category.
 10. The method of claim 9,further comprising: in response to classifying the first code fragmentas the first code category, performing a security analysis using thefirst code fragment, wherein the first code category is a securityrelevant category.
 11. The method of claim 9, further comprising:training the abstract syntax tree classifier using training codefragments comprising first code fragments represented in the firstprogramming language and second code fragments represented in the secondprogramming language.
 12. The method of claim 11, further comprising:obtaining a third code fragment comprising third language featuresrepresented in a third programming language, wherein the training codefragments exclude code fragments represented in the third programminglanguage; generating a third universal abstract syntax tree for thethird code fragment, wherein the third universal abstract syntax treecomprises at least one language independent feature; generating, usingthe graph embedding model, a third plurality of vectors for the thirduniversal abstract syntax tree; and classifying, by executing theabstract syntax tree classifier on the third plurality of vectors, thecode fragment as a third code category.
 13. The method of claim 9,wherein the first universal abstract syntax tree comprises a subtree fora first language feature of the first plurality of language features,wherein the first language feature is one selected from a groupconsisting of a control flow statement and a declaration statement, andwherein the first plurality of vectors comprises a vector for thesubtree.
 14. The method of claim 13, wherein the first universalabstract syntax tree comprises a plurality of nodes, wherein the subtreecomprises a first node of the plurality of nodes, and wherein generatingthe first plurality of vectors comprises: identifying, for the firstnode, a subset of the plurality of nodes in a local neighborhood of thefirst node; and aggregating information from a subset of the firstplurality of language features corresponding to the subset of theplurality of nodes in the local neighborhood of the first node.
 15. Themethod of claim 9, further comprising: generating a language specificabstract syntax tree for the first code fragment and the firstprogramming language; and converting the language specific abstractsyntax tree to the first universal abstract syntax tree.
 16. The methodof claim 9, wherein the first universal abstract syntax tree furthercomprises a first language specific feature that is specific to thefirst programming language, and wherein the second universal abstractsyntax tree further comprises a second language specific feature that isspecific to the second programming language.
 17. A method comprising:obtaining a first code fragment comprising a first plurality of languagefeatures represented in a first programming language; sending the firstcode fragment to a universal code fragment classifier configured toperform; generating a first universal abstract syntax tree for the firstcode fragment, wherein the first universal abstract syntax treecomprises at least one language independent feature, generating, using agraph embedding model, a first plurality of vectors for the firstuniversal abstract syntax tree, classifying, by executing an abstractsyntax tree classifier on the first plurality of vectors, the first codefragment as a first code category, and transmitting the first codecategory for the first code fragment; and receiving, from the universalcode fragment classifier, the first code category for the first codefragment.
 18. The method of claim 17, wherein the universal codefragment classifier is further configured to: in response to classifyingthe first code fragment as the first code category, perform a securityanalysis using the first code fragment, wherein the first code categoryis a security relevant category.
 19. The method of claim 17, wherein theuniversal code fragment classifier is further configured to: train theabstract syntax tree classifier using training code fragments comprisingfirst code fragments represented in the first programming language andsecond code fragments represented in the second programming language,obtain a third code fragment comprising third language featuresrepresented in a third programming language, wherein the training codefragments exclude code fragments represented in the third programminglanguage, generate a third universal abstract syntax tree for the thirdcode fragment, wherein the third universal abstract syntax treecomprises at least one language independent feature, generate, using thegraph embedding model, a third plurality of vectors for the thirduniversal abstract syntax tree, and classify, by executing the abstractsyntax tree classifier on the third plurality of vectors, the codefragment as a third code category.
 20. The method of claim 17, whereinthe first universal abstract syntax tree comprises a subtree for a firstlanguage feature of the first plurality of language features, whereinthe first language feature is one selected from a group consisting of acontrol flow statement and a declaration statement, and wherein thefirst plurality of vectors comprises a vector for the subtree.